There are a number of industries that utilize very high precision processes in their businesses. Medical device, semiconductor, and machining equipment manufacturers—among others—often produce or utilize devices having very small-scale (e.g., sub-micron level) features and tolerances. In such applications, precise and accurate measurements are critical to the functionality and marketability of device.
Over time, a number of devices and methods have been developed for measuring and profiling such small-scale devices. Such characterization is sometimes referred to as metrology. Unfortunately, depending upon how small a particular device may be, a number of conventional metrology methods are destructive in nature. Such methods rely on a partial or complete disassembly or dissection of the device in order to provide an accurate measurement. Often, such processes are partially or completely manual, and the device being measured or profiled is completely or effectively destroyed in the process. Although they tend to provide highly accurate characterizations, these approaches are not of much use for high-volume commercial applications.
Other non-destructive metrology methods have been developed to provide small-scale measurement and characterization. One of the more common such methods is the use of profilometry. Profilometry is typically a non-destructive process that involves physical movement of some fine-gauge sensory instrument (e.g., a stylus) along the surface of a device being profiled. Profilometry provides a physical measurement or characterization of a device, relative to some reference or starting point. Thus, although not destructive in nature, profilometry and other similar approaches provide only vector data. Most such systems do not provide any function or capability for checking the accuracy of the profile measurement.
In response, the accuracy of profilometry and other similar approaches is often checked via statistical analysis. Historical data, taken from measurements at several locations on a device, or several devices within a lot, is compiled for analysis. Often, such data is organized and analyzed by conventional metrics such as range and standard deviation. Unfortunately, such metrics are typically subjective in nature, data-intensive, and may be impacted by a number of variables (e.g., the number of data points compiled). Analysis methods utilizing these metrics typically do not provide an immediate and clearly identifiable indicator of a subtle shift from accurate to inaccurate measurement. Instead, inaccuracies may go undetected as gradual shifts in a trend line of range or standard deviation data.
As a result, there is a need for a versatile system for analyzing the accuracy of profilometry and other similar data—a system that provides immediate and clear indication of data inaccuracies in a simple, efficient and effective manner.